def test_draco(): n_points = 12 img = sphere_tissue_image(size=100, n_points=n_points) draco = DracoMesh(img) assert draco.point_topomesh.nb_wisps(0) == n_points + 1 draco.delaunay_adjacency_complex(surface_cleaning_criteria=[]) image_tetrahedra = np.sort(draco.image_cell_vertex.keys()) image_tetrahedra = image_tetrahedra[image_tetrahedra[:, 0] != 1] draco_tetrahedra = np.sort([ list(draco.triangulation_topomesh.borders(3, t, 3)) for t in draco.triangulation_topomesh.wisps(3) ]) delaunay_consistency = jaccard_index(image_tetrahedra, draco_tetrahedra) draco.adjacency_complex_optimization(n_iterations=2) assert draco.triangulation_topomesh.nb_region_neighbors(0, 2) == n_points image_tetrahedra = np.sort(draco.image_cell_vertex.keys()) image_tetrahedra = image_tetrahedra[image_tetrahedra[:, 0] != 1] draco_tetrahedra = np.sort([ list(draco.triangulation_topomesh.borders(3, t, 3)) for t in draco.triangulation_topomesh.wisps(3) ]) draco_consistency = jaccard_index(image_tetrahedra, draco_tetrahedra) # print delaunay_consistency,' -> ',draco_consistency assert draco_consistency == 1 or (draco_consistency >= 0.9 and draco_consistency > delaunay_consistency) triangular = ['star', 'remeshed', 'projected', 'regular', 'flat'] image_dual_topomesh = draco.dual_reconstruction( reconstruction_triangulation=triangular, adjacency_complex_degree=3) image_volumes = array_dict( nd.sum(np.ones_like(img), img, index=np.unique(img)[1:]) * np.prod(img.voxelsize), np.unique(img)[1:]) compute_topomesh_property(image_dual_topomesh, 'volume', 3) draco_volumes = image_dual_topomesh.wisp_property('volume', 3) for c in image_dual_topomesh.wisps(3): assert np.isclose(image_volumes[c], draco_volumes[c], 0.33)
def test_tetrahedrization_optimization(): topomesh = octahedron_tetrahedra() compute_tetrahedrization_topological_properties(topomesh) compute_tetrahedrization_geometrical_properties(topomesh) tetrahedrization_topomesh_add_exterior(topomesh) target_tetrahedra = [] target_tetrahedra += [(2, 3, 5, 6)] target_tetrahedra += [(3, 4, 5, 6)] target_tetrahedra += [(2, 3, 5, 7)] target_tetrahedra += [(3, 4, 5, 7)] target_tetrahedra += [(1, 2, 3, 6)] target_tetrahedra += [(1, 3, 4, 6)] target_tetrahedra += [(1, 4, 5, 6)] target_tetrahedra += [(1, 2, 5, 6)] target_tetrahedra += [(1, 2, 3, 7)] target_tetrahedra += [(1, 3, 4, 7)] target_tetrahedra += [(1, 4, 5, 7)] target_tetrahedra += [(1, 2, 5, 7)] target_tetrahedra = dict(zip(target_tetrahedra, np.zeros((4, 3)))) kwargs = {} kwargs['simulated_annealing_initial_temperature'] = 0.1 kwargs['simulated_annealing_lambda'] = 0.9 kwargs['simulated_annealing_minimal_temperature'] = 0.09 kwargs['n_iterations'] = 1 topomesh = tetrahedrization_topomesh_topological_optimization( topomesh, image_cell_vertex=target_tetrahedra, omega_energies=dict(image=1.0), **kwargs) tetrahedrization_topomesh_remove_exterior(topomesh) assert topomesh.nb_wisps(3) == 4 for t in topomesh.wisps(3): print t, list(topomesh.borders(3, t, 3)) optimized_tetras = np.sort( [list(topomesh.borders(3, t, 3)) for t in topomesh.wisps(3)]) target_tetras = np.sort(target_tetrahedra.keys()) target_tetras = target_tetras[target_tetras[:, 0] != 1] assert jaccard_index(optimized_tetras, target_tetras) == 1.0
def test_draco(): n_points = 12 img = sphere_tissue_image(size=100,n_points=n_points) draco = DracoMesh(img) assert draco.point_topomesh.nb_wisps(0) == n_points+1 draco.delaunay_adjacency_complex(surface_cleaning_criteria = []) image_tetrahedra = np.sort(draco.image_cell_vertex.keys()) image_tetrahedra = image_tetrahedra[image_tetrahedra[:,0] != 1] draco_tetrahedra = np.sort([list(draco.triangulation_topomesh.borders(3,t,3)) for t in draco.triangulation_topomesh.wisps(3)]) delaunay_consistency = jaccard_index(image_tetrahedra, draco_tetrahedra) draco.adjacency_complex_optimization(n_iterations=2) assert draco.triangulation_topomesh.nb_region_neighbors(0,2) == n_points image_tetrahedra = np.sort(draco.image_cell_vertex.keys()) image_tetrahedra = image_tetrahedra[image_tetrahedra[:,0] != 1] draco_tetrahedra = np.sort([list(draco.triangulation_topomesh.borders(3,t,3)) for t in draco.triangulation_topomesh.wisps(3)]) draco_consistency = jaccard_index(image_tetrahedra, draco_tetrahedra) # print delaunay_consistency,' -> ',draco_consistency assert draco_consistency == 1 or (draco_consistency >= 0.9 and draco_consistency > delaunay_consistency) triangular = ['star','remeshed','projected','regular','flat'] image_dual_topomesh = draco.dual_reconstruction(reconstruction_triangulation = triangular, adjacency_complex_degree=3) image_volumes = array_dict(nd.sum(np.ones_like(img),img,index=np.unique(img)[1:])*np.prod(img.resolution),np.unique(img)[1:]) compute_topomesh_property(image_dual_topomesh,'volume',3) draco_volumes = image_dual_topomesh.wisp_property('volume',3) for c in image_dual_topomesh.wisps(3): assert np.isclose(image_volumes[c],draco_volumes[c],0.33)
def test_tetrahedrization_optimization(): topomesh = octahedron_tetrahedra() compute_tetrahedrization_topological_properties(topomesh) compute_tetrahedrization_geometrical_properties(topomesh) tetrahedrization_topomesh_add_exterior(topomesh) target_tetrahedra = [] target_tetrahedra += [(2,3,5,6)] target_tetrahedra += [(3,4,5,6)] target_tetrahedra += [(2,3,5,7)] target_tetrahedra += [(3,4,5,7)] target_tetrahedra += [(1,2,3,6)] target_tetrahedra += [(1,3,4,6)] target_tetrahedra += [(1,4,5,6)] target_tetrahedra += [(1,2,5,6)] target_tetrahedra += [(1,2,3,7)] target_tetrahedra += [(1,3,4,7)] target_tetrahedra += [(1,4,5,7)] target_tetrahedra += [(1,2,5,7)] target_tetrahedra = dict(zip(target_tetrahedra,np.zeros((4,3)))) kwargs = {} kwargs['simulated_annealing_initial_temperature'] = 0.1 kwargs['simulated_annealing_lambda'] = 0.9 kwargs['simulated_annealing_minimal_temperature'] = 0.09 kwargs['n_iterations'] = 1 topomesh = tetrahedrization_topomesh_topological_optimization(topomesh,image_cell_vertex=target_tetrahedra,omega_energies=dict(image=1.0),**kwargs) tetrahedrization_topomesh_remove_exterior(topomesh) assert topomesh.nb_wisps(3) == 4 for t in topomesh.wisps(3): print t, list(topomesh.borders(3,t,3)) optimized_tetras = np.sort([list(topomesh.borders(3,t,3)) for t in topomesh.wisps(3)]) target_tetras = np.sort(target_tetrahedra.keys()) target_tetras = target_tetras[target_tetras[:,0] != 1] assert jaccard_index(optimized_tetras,target_tetras) == 1.0